Computer vision-based techniques and path planning strategy in a slope monitoring system using unmanned aerial vehicle

Unmanned aerial vehicle is a typical field robot which can work in many unstructured environments like mines, forests, and even radiation areas. In our mine monitoring system built in a northeast province of China, special designed unmanned aerial vehicle is applied to take photos and perceive the environment. We select a series of image-based techniques to process aerial pictures to monitor the slope. The visual features are initially refined by histogram equalization. Then, the rocks and cracks can be detected by different digital image processing operators, like Canny, so as to assess displacements. Advanced semantic segmentation model, U-Net, is also selected to process the problem. Experimental results show that both Canny and U-Net can perceive the edges in pictures effectively, better than other operators. In addition, we model the inspection mission for mine slopes into a traveling salesman problem, then plan the path for unmanned aerial vehicle by swarm intelligence-based optimization.

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Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽

10.3390/s21134442 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4442

Author(s):

Zijie Niu ◽

Juntao Deng ◽

Xu Zhang ◽

Jun Zhang ◽

Shijia Pan ◽

...

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicle ◽

Semantic Segmentation ◽

Dynamic Monitoring ◽

Support Vector ◽

Distribution Maps ◽

Time Operation ◽

Aerial Vehicle ◽

Uav Images ◽

Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

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Road Image Segmentation using Unmanned Aerial Vehicle Images and DeepLab V3+ Semantic Segmentation Model

10.1109/iccsce52189.2021.9530950 ◽

2021 ◽

Author(s):

Mat Nizam Mahmud ◽

Muhammad Khusairi Osman ◽

Ahmad Puad Ismail ◽

Fadzil Ahmad ◽

Khairul Azman Ahmad ◽

...

Keyword(s):

Image Segmentation ◽

Unmanned Aerial Vehicle ◽

Semantic Segmentation ◽

Aerial Vehicle

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Smart Environment Monitoring System using Unmanned Aerial Vehicle in Bangladesh

EAI Endorsed Transactions on Smart Cities ◽

10.4108/eai.18-8-2020.165995 ◽

2018 ◽

pp. 165995

Author(s):

Md. Al-Farabi ◽

Muntasir Chowdhury ◽

Md. Readuzzaman ◽

Md. Hossain ◽

Saifur Sabuj ◽

...

Keyword(s):

Monitoring System ◽

Unmanned Aerial Vehicle ◽

Smart Environment ◽

Environment Monitoring ◽

Aerial Vehicle

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A Cauchy mutant pigeon-inspired optimization–based multi-unmanned aerial vehicle path planning method

Measurement and Control ◽

10.1177/0020294019885155 ◽

2020 ◽

Vol 53 (1-2) ◽

pp. 83-92 ◽

Cited By ~ 3

Author(s):

Bo Hang Wang ◽

Dao Bo Wang ◽

Zain Anwar Ali

Keyword(s):

Path Planning ◽

Unmanned Aerial Vehicle ◽

Optimization Algorithm ◽

Vehicle Group ◽

Flight Duration ◽

Planning Strategy ◽

Optimum Position ◽

Cauchy Mutation ◽

Aerial Vehicle ◽

Vehicle Path

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.

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Analysis of an Unmanned Aerial Vehicle Monitoring System for Resurveying of Shipping Routes

Transport and Aerospace Engineering ◽

10.1515/tae-2016-0012 ◽

2016 ◽

Vol 3 (1) ◽

pp. 102-111

Author(s):

Aleksandrs Urbahs ◽

Rima Mickevičienė ◽

Vasilij Djačkov ◽

Kristīne Carjova ◽

Valdas Jankūnas ◽

...

Keyword(s):

Monitoring System ◽

Unmanned Aerial Vehicle ◽

Survey Methods ◽

Environment Monitoring ◽

Vehicle Monitoring ◽

Hydrographic Survey ◽

Survey System ◽

Aerial Vehicle ◽

Security Operations ◽

Marine Safety

Abstract The paper gives brief description of the conventional and innovative hydrography survey methods and constraints connected with the realization. Proposed hydrographic survey system based on the use of Unmanned Aerial and Maritime systems provides functionality to conduct hydrographic measurements and environment monitoring. System can be easily adapted to fulfil marine safety and security operations, e.g. intrusion threat monitoring, hazardous pollutions monitoring and prevention operations, icing conditions monitoring.

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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Sensors ◽

10.3390/s21196540 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6540

Author(s):

Qian Pan ◽

Maofang Gao ◽

Pingbo Wu ◽

Jingwen Yan ◽

Shilei Li

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicle ◽

Recognition Accuracy ◽

Yellow Rust ◽

Semantic Segmentation ◽

High Accuracy ◽

Economic Losses ◽

Support Vector ◽

Aerial Vehicle ◽

Uav Images

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

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A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner

Remote Sensing ◽

10.3390/rs13234811 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4811

Author(s):

Rudolf Urban ◽

Martin Štroner ◽

Lenka Línková

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Reference Points ◽

New Method ◽

Color Information ◽

Dense Point ◽

Aerial Vehicle ◽

Systematic Shift ◽

Precision Testing ◽

Better Than

Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.

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